{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook performs sanity check to make sure baseline (MMSE, Classic demod, Viterbi) working as expected.\n",
    "\n",
    "* Inputs: (CFO + Channel Inference + AWGN) signals\n",
    "* Outputs: Estimated message bits\n",
    "\n",
    "Tests:\n",
    "  * Baseline on inputs with no CFO, no Channel Interference\n",
    "  * Baseline on inputs with no CFO, channel_tap = 2\n",
    "  * Baseline on inputs with CFO = 1/100, channel_tap =2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages from other direction. Itis necessary if the project is structured as:\n",
    "import multiprocessing as mp\n",
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "    \n",
    "import numpy as np\n",
    "import scipy\n",
    "from radioml.models import Baseline, MMSEEqualizer\n",
    "from radioml.metrics import get_ber_bler\n",
    "from radioml.dataset import RadioDataGenerator\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pylab\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define paramters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "MOD_SCHEME   = 'QPSK'  # or {'BPSK', 'QPSK', 'QAM16', 'QAM64'}\n",
    "NUM_PACKETS  = 100\n",
    "PREAMBLE_LEN = 40\n",
    "DATA_LEN     = 200\n",
    "SNRs         = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Baseline (MSSE, Classic Demodulation, Viterbi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mmse = MMSEEqualizer(equalizer_order=5, update_rate=0.1)\n",
    "baseline = Baseline(equalizer=mmse, modulation_scheme=MOD_SCHEME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define benchmark func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_benchmark(radio, num_packets, snr_range, omega, channel_len, seed=None):\n",
    "    bit_error_rates, block_error_rates = [], []\n",
    "    for snr in snr_range:\n",
    "        generator = radio.end2end_data_generator(OMEGA, snr, \n",
    "                                                 batch_size=num_packets, \n",
    "                                                 seed=seed)\n",
    "        [preambles, corrupted_packets], [message_bits, w, channels] = next(generator)\n",
    "        \n",
    "        # Extract preamble_conv and convert to complex\n",
    "        preambles = preambles.view(complex)\n",
    "        convolved_preamble = np.array(corrupted_packets[:, :radio.preamble_len, :]).view(complex)\n",
    "        convolved_data     = np.array(corrupted_packets[:, radio.preamble_len:, :]).view(complex)\n",
    "\n",
    "        # Esimate message bits with Baseline\n",
    "        with mp.Pool(mp.cpu_count()) as pool:\n",
    "            baseline_results = pool.starmap(baseline,[(i,j,k) for i,j,k in \n",
    "                                        zip(convolved_data, convolved_preamble, preambles)])\n",
    "            baseline_results = np.array(baseline_results)\n",
    "\n",
    "        ber, bler = get_ber_bler(baseline_results, np.squeeze(message_bits, -1))\n",
    "        print('SNR_dB = {:5.2f} : BER = {:.10f} | BLER ={:.10f} '.format(snr, ber, bler))\n",
    "        bit_error_rates.append([ber, ber])\n",
    "        block_error_rates.append([bler, bler])\n",
    "        \n",
    "    return bit_error_rates, block_error_rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Baseline with No Intersymbol Interference (Channel_len = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR_dB =  0.00 : BER = 0.2773000000 | BLER =1.0000000000 \n",
      "SNR_dB =  2.00 : BER = 0.1298000000 | BLER =1.0000000000 \n",
      "SNR_dB =  4.00 : BER = 0.0342500000 | BLER =0.9100000000 \n",
      "SNR_dB =  6.00 : BER = 0.0039500000 | BLER =0.2800000000 \n",
      "SNR_dB =  8.00 : BER = 0.0005500000 | BLER =0.0600000000 \n",
      "SNR_dB = 10.00 : BER = 0.0000500000 | BLER =0.0100000000 \n",
      "SNR_dB = 12.00 : BER = 0.0000000000 | BLER =0.0000000000 \n"
     ]
    }
   ],
   "source": [
    "CHANNEL_LEN  = 1        # Ignore channel interference\n",
    "OMEGA        = 1/10000  # no CFO.\n",
    "radio        = RadioDataGenerator(DATA_LEN, \n",
    "                                  PREAMBLE_LEN, \n",
    "                                  CHANNEL_LEN, \n",
    "                                  modulation_scheme=MOD_SCHEME)\n",
    "bers, blers = run_benchmark(radio, \n",
    "                            NUM_PACKETS,\n",
    "                            snr_range=SNRs, \n",
    "                            omega=OMEGA,\n",
    "                            channel_len=CHANNEL_LEN,\n",
    "                            seed=2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Baseline with Intersymbol Interference (Channel_len = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR_dB =  0.00 : BER = 0.3097500000 | BLER =1.0000000000 \n",
      "SNR_dB =  2.00 : BER = 0.1862000000 | BLER =1.0000000000 \n",
      "SNR_dB =  4.00 : BER = 0.0915500000 | BLER =0.9900000000 \n",
      "SNR_dB =  6.00 : BER = 0.0375500000 | BLER =0.7800000000 \n",
      "SNR_dB =  8.00 : BER = 0.0158500000 | BLER =0.6400000000 \n",
      "SNR_dB = 10.00 : BER = 0.0081000000 | BLER =0.5000000000 \n",
      "SNR_dB = 12.00 : BER = 0.0037500000 | BLER =0.3500000000 \n"
     ]
    }
   ],
   "source": [
    "CHANNEL_LEN  = 2\n",
    "OMEGA        = 1/10000  # no CFO.\n",
    "radio        = RadioDataGenerator(DATA_LEN, PREAMBLE_LEN, \n",
    "                                  CHANNEL_LEN, \n",
    "                                  modulation_scheme=MOD_SCHEME)\n",
    "\n",
    "bers1, blers1 = run_benchmark(radio, \n",
    "                              NUM_PACKETS,\n",
    "                              snr_range=SNRs, \n",
    "                              omega=OMEGA, \n",
    "                              channel_len=CHANNEL_LEN,\n",
    "                              seed=2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Baseline with Intersymbol Interference (Channel_len = 2), CFO = 1/200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR_dB =  0.00 : BER = 0.3743500000 | BLER =1.0000000000 \n",
      "SNR_dB =  2.00 : BER = 0.2822500000 | BLER =1.0000000000 \n",
      "SNR_dB =  4.00 : BER = 0.1928000000 | BLER =1.0000000000 \n",
      "SNR_dB =  6.00 : BER = 0.1268500000 | BLER =0.9100000000 \n",
      "SNR_dB =  8.00 : BER = 0.0966500000 | BLER =0.8100000000 \n",
      "SNR_dB = 10.00 : BER = 0.0766500000 | BLER =0.7400000000 \n",
      "SNR_dB = 12.00 : BER = 0.0667000000 | BLER =0.7000000000 \n"
     ]
    }
   ],
   "source": [
    "CHANNEL_LEN  = 2\n",
    "OMEGA        = 1/200  # no CFO.\n",
    "radio        = RadioDataGenerator(DATA_LEN, \n",
    "                                  PREAMBLE_LEN, \n",
    "                                  CHANNEL_LEN, \n",
    "                                  modulation_scheme=MOD_SCHEME)\n",
    "bers2, blers2 = run_benchmark(radio, \n",
    "                              NUM_PACKETS,\n",
    "                              snr_range=SNRs, \n",
    "                              omega=OMEGA, \n",
    "                              channel_len=CHANNEL_LEN, \n",
    "                              seed=2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_ber_bler(ax1, ax2, ber_logs, bler_logs, snr_range, title, style='*-'):\n",
    "    ax1.plot(snr_range, np.array(ber_logs).T[1, :], style, label=title,)\n",
    "    ax1.set_xlabel('SNR (in dB)')\n",
    "    ax1.set_ylabel('Bit Error Rate (BER)')\n",
    "    ax1.grid(True,'both')\n",
    "    ax1.set_xlim(np.min(snr_range), np.max(snr_range))\n",
    "    \n",
    "    ax2.plot(snr_range, np.array(bler_logs).T[1,:], style, label=title)\n",
    "    ax2.set_xlabel('SNR (in dB)')\n",
    "    ax2.set_ylabel('Block Error Rate (BLER)')\n",
    "    ax2.set_xlim(np.min(snr_range), np.max(snr_range))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 7))\n",
    "fig.suptitle('Evaluate Baseline (MMSE + Classic Demod + Viterbi) on different signals', \n",
    "             fontsize='x-large')\n",
    "\n",
    "visualize_ber_bler(ax1, ax2, bers, blers, SNRs, 'Inputs 1 (No CFO, No Intersymbol Interference)', '.-')\n",
    "visualize_ber_bler(ax1, ax2, bers1, blers1, SNRs, 'Inputs 2 (No CFO, Channel length = 2)', '*-')\n",
    "visualize_ber_bler(ax1, ax2, bers2, blers2, SNRs,  'Inputs 3 (CFO=1/200, Channel length = 2)', 'v-')\n",
    "\n",
    "ax1.semilogy()\n",
    "ax2.semilogy()\n",
    "ax1.legend(loc=3)\n",
    "_ =ax2.legend(loc=3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deepcom",
   "language": "python",
   "name": "deepcom"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
